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Ensemble Learning to Improve Machine Learning Results

#artificialintelligence

Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance (bagging), bias (boosting), or improve predictions (stacking). Most ensemble methods use a single base learning algorithm to produce homogeneous base learners, i.e. learners of the same type, leading to homogeneous ensembles. There are also some methods that use heterogeneous learners, i.e. learners of different types, leading to heterogeneous ensembles. In order for ensemble methods to be more accurate than any of its individual members, the base learners have to be as accurate as possible and as diverse as possible. Bagging stands for bootstrap aggregation.


Discussion of Ensemble Learning under the Era of Deep Learning

arXiv.org Artificial Intelligence

Due to the dominant position of deep learning (mostly deep neural networks) in various artificial intelligence applications, recently, ensemble learning based on deep neural networks (ensemble deep learning) has shown significant performances in improving the generalization of learning system. However, since modern deep neural networks usually have millions to billions of parameters, the time and space overheads for training multiple base deep learners and testing with the ensemble deep learner are far greater than that of traditional ensemble learning. Though several algorithms of fast ensemble deep learning have been proposed to promote the deployment of ensemble deep learning in some applications, further advances still need to be made for many applications in specific fields, where the developing time and computing resources are usually restricted or the data to be processed is of large dimensionality. An urgent problem needs to be solved is how to take the significant advantages of ensemble deep learning while reduce the required time and space overheads so that many more applications in specific fields can benefit from it. For the alleviation of this problem, it is necessary to know about how ensemble learning has developed under the era of deep learning. Thus, in this article, we present discussion focusing on data analyses of published works, the methodology and unattainability of traditional ensemble learning, and recent developments of ensemble deep learning. We hope this article will be helpful to realize the technical challenges faced by future developments of ensemble learning under the era of deep learning.


Improving Predictions with Ensemble Model

#artificialintelligence

"Alone we can do so little and together we can do much" - a phrase from Helen Keller during 50's is a reflection of achievements and successful stories in real life scenarios from decades. Same thing applies with most of the cases from innovation with big impacts and with advanced technologies world. The machine Learning domain is also in the same race to make predictions and classification in a more accurate way using so called ensemble method and it is proved that ensemble modeling offers one of the most convincing way to build highly accurate predictive models. Ensemble methods are learning models that achieve performance by combining the opinions of multiple learners. Typically, an ensemble model is a supervised learning technique for combining multiple weak learners or models to produce a strong learner with the concept of Bagging and Boosting for data sampling.


Improving Predictions with Ensemble Model

@machinelearnbot

"Alone we can do so little and together we can do much" - a phrase from Helen Keller during 50's is a reflection of achievements and successful stories in real life scenarios from decades. Same thing applies with most of the cases from innovation with big impacts and with advanced technologies world. The machine Learning domain is also in the same race to make predictions and classification in a more accurate way using so called ensemble method and it is proved that ensemble modeling offers one of the most convincing way to build highly accurate predictive models. Ensemble methods are learning models that achieve performance by combining the opinions of multiple learners. Typically, an ensemble model is a supervised learning technique for combining multiple weak learners or models to produce a strong learner with the concept of Bagging and Boosting for data sampling.


Ensemble Learning: Stacking, Blending & Voting

#artificialintelligence

We have heard the phrase "unity is strength", whose meaning can be transferred to different areas of life. Sometimes correct answers to a specific problem are supported by several sources and not just one. This is what Ensemble Learning tries to do, that is, to put together a group of ML models to improve solutions to specific problems. Throughout this blog, we will learrn what Ensemble Learning is, what are the types of Ensembles that exist and we will specifically address Voting and Stacking Ensembles. Ensemble Learning refers to the use of ML algorithms jointly to solve classification and/or regression problems mainly. These algorithms can be the same type (homogeneous Ensemble Learning) or different types (heterogeneous Ensemble Learning).